Reproduction provoquée chez les poissons : théorie et pratique

Version anglaise disponible dans la Bibliothèque numérique du CRDI : Theory and practice of induced breeding in fishVersion espagnole disponible dans la Bibliothèque numérique du CRDI : Teoría y práctica de la reproducción inducida en los pecesVersion arabe dans la bibliothèqueVersion chinoise dans la bibliothèqu

Theory and practice of induced breeding in fish

French version available in IDRC Digital Library: Reproduction provoquée chez les poissons : théorie et pratiqueSpanish version available in IDRC Digital Library: Teoría y práctica de la reproducción inducida en los pecesArabic version available in IDRC Digital LibraryChinese version available in IDRC Digital Librar

General And Comparative Physiology

New Jerseyxivi, 848 p.: illus.; 25 cm

Ceneral And Comparative Physiology

xiv 848 hal ; 26 c

Teoría y práctica de la reproducción inducida en los peces

Versión en inglés disponible en la Biblioteca Digital del IDRC: Theory and practice of induced breeding in fishVersión en francés disponible en la Biblioteca Digital del IDRC: Reproduction provoquée chez les poissons : théorie et pratiqueVersión arábiga en la bibliotecaVersión china en la bibliotec

Theory and practice of induced breeding in fish [Chinese version]

English version available in IDRC Digital LibraryFrench version available in IDRC Digital Library: Reproduction provoquée chez les poissons : théorie et pratiqueSpanish version available in IDRC Digital Library: Teoría y práctica de la reproducción inducida en los pecesArabic version available in IDRC Digital Librar

Theory and practice of induced breeding in fish [Arabic version]

Library has English versionLibrary has French version: Reproduction provoquée chez les poissons : théorie et pratiqueLibrary has Spanish version: Teoría y práctica de la reproducción inducida en los pecesLibrary has Chinese versio

Evaluation of a Data Assimilation System for Land Surface Models using CLM4. 5

The magnitude and persistence of land carbon (C) pools influence long‐term climate feedbacks. Interactive ecological processes influence land C pools and our understanding of these processes is imperfect so land surface models have errors and biases when compared to each other and to real observations. Here we implement an Ensemble Adjustment Kalman Filter (EAKF), a sequential state data assimilation technique to reduce these errors and biases. We implement the EAKF using the Data Assimilation Research Testbed coupled with the Community Land Model (CLM 4.5 in CESM 1.2). We assimilated simulated and real satellite observations for a site in central New Mexico, United States. A series of observing system simulation experiments allowed assessment of the data assimilation system without model error. This showed that assimilating biomass and leaf area index observations decreased model error in C dynamics forecasts (29% using biomass observations and 40% using leaf area index observations) and that assimilation in combination shows greater improvement (51% using both observation streams). Assimilating real observations highlighted likely model structural errors and we implemented an adaptive model‐variance‐inflation technique to allow the model to track the observations. Monthly and longer model forecasts using real observations were improved relative to forecasts without data assimilation. The reliable forecast lead‐time varied by model pool and is dependent on how tightly the C pool is coupled to meteorologically driven processes. The EAKF and similar state data assimilation techniques could reduce errors in projections of the land C sink and provide more robust forecasts of C pools and land‐atmosphere exchanges